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Abstract Microbes are pervasive and their interaction with each other and the environment can impact fields as diverse as health and agriculture. While network inference and related algorithms that use abundance data from pyrosequencing can infer microbial interaction networks, the ambiguity surrounding the actual underlying networks hampers the validation of these algorithms. This study introduces a generative model to synthesize both the underlying interactive network and observable abundance data, serving as a test bed for the existing and future network inference algorithms. We tested our generative model with four typical network inference algorithms; our results suggest that none of these algorithms demonstrate adequate accuracy for inferring ecologies of non-commensalistic species, either mutualistic or competitive. We further explored the potential for predictability by combining existing algorithms with an oracle algorithm built by fusing the results of several existing algorithms. The oracle algorithm reveals promising improvements in predictability, although it falls short when applied to networks characterized by dense interspecies taxa interactions. Our work underscores the need for the continued development and validation of algorithms to unravel the intricacies of microbial interaction networks.

Abstract Data categorization is a top concern in medical data to predict and detect illnesses; thus, it is applied in modern healthcare informatics. In modern informatics, machine learning and deep learning models have enjoyed great attention for categorizing medical data and improving illness detection. However, the existing techniques, such as features with high dimensionality, computational complexity, and long-term execution duration, raise fundamental problems. This study presents a novel classification model employing metaheuristic methods to maximize efficient positives on Chronic Kidney Disease diagnosis. The medical data is initially massively pre-processed, where the data is purified with various mechanisms, including missing values resolution, data transformation, and the employment of normalization procedures. The focus of such processes is to leverage the handling of the missing values and prepare the data for deep analysis. We adopt the Binary Grey Wolf Optimization method, a reliable subset selection feature using metaheuristics. This operation is aimed at improving illness prediction accuracy. In the classification step, the model adopts the Extreme Learning Machine with hidden nodes through data optimization to predict the presence of CKD. The complete classifier evaluation employs established measures, including recall, specificity, kappa, F-score, and accuracy, in addition to the feature selection. Data related to the study show that the proposed approach records high levels of accuracy, which is better than the existing models.

The industrial sector is vital to economic progress, yet industrial pollution poses environmental and economic concerns. The purpose of the study was to investigate the influence of green industrial transformation in reducing Pakistan's carbon intensity between 1975 and 2020. Carbon emissions are considered an endogenous construct, while foreign direct investment (FDI) inflows, technological innovation, green industrial transformation, environmental legislation, and research and development (R&D) investment are possible mediators. The association between variables is assessed using the robust least-squares approach. Green industrial transformation is connected with lower carbon emissions, yet technical innovation, R&D investment, and inbound FDI raise a country's carbon emissions. The findings support the pollution haven hypothesis in a country. The causality estimates indicate that inward FDI contributes to environmental regulations; green industrial transformation directly relates to inbound FDI and R&D expenditures; and technological innovations correspond to inbound FDI, R&D expenditures, industrial ecofriendly progression, and environmental standards. According to the impulse response function, environmental policies are anticipated to have a differential effect on carbon emissions in 2023, 2024, 2028–2030, while they are likely to decrease in the years 2025–2027 and 2031 forward. Additionally, inward FDI and technology advancements would almost certainly result in a rise in carbon emissions over time. Green industrial transitions are projected to result in a ten-year reduction in carbon emissions. The variance decomposition analysis indicates that eco-friendly industrial adaptations would likely have the largest variance error shock on carbon emissions (11.747%), followed by inbound FDI, technological advancements, and regulatory changes, with R&D spending having a minimal impact over time. Pakistan's economy should foster a green industrial revolution to avoid pollution and increase environmental sustainability to meet its environmental goals.

Key Clinical Message Methicillin‐resistant staph aureus (MRSA) infections are challenging to treat, and with the emergence of community‐associated MRSA (CA‐MRSA) strains, early consideration of this pathogen in populations without typical risk factors is critical. Here we present a case of CA‐MRSA pneumonia that resulted in Community‐acquired pneumonia (CAP) with septic shock, pyelonephritis, and muscle abscess.

The rise in stable coronary artery disease (CAD) due to improved survival rates and population growth has increased patient numbers, straining healthcare systems. Machine learning (ML) models are being developed to predict and identify individual risk factors for early treatment, reducing harm to individuals and families. These models can predict hospitalizations, enable close monitoring of high-risk patients, and optimize medical care. Researchers are developing robust models based on ML algorithms and real-world clinical data to aid in early detection, contributing to AI research in healthcare. Advanced ML models analyze medical imaging, genetic markers, lifestyle, and environmental factors to accurately predict coronary heart disease (CHD) start and progression. Our research introduces four novel models based on two-class Logistic Regression (two-class LR), two-class Neural Network (two-class NN), two-class Decision Jungle (two-class DJ), and two-class Boosted DT (two-class BDT). Our comparative analysis reveals that the two-class Boosted DT model is the most effective, achieving an AUC score of 0.991. This model excels in real-time monitoring by predicting minor changes in patient’s health markers, allowing for timely adjustments in treatment plans. It optimizes medication selection, dosing, and intervention timing based on patient characteristics, improving therapeutic efficacy and reducing side effects. The study reveals the transformative potential of these advanced ML models in CAD prediction and management. By focusing on feature selection, algorithm improvement, and integration, our models analyze medical imaging, genetic markers, lifestyle, and environmental factors to accurately predict the onset and progression of CHD. This research proposes valuable insights into the capabilities of these models to revolutionize disease detection and management, ensuring reliable and timely healthcare interventions across various datasets.

DevOps is a collection of principles, practices, and cultural philosophies aimed at enhancing communication and collaboration among information and technology operations groups and software development. The key objective of DevOps is to simplify the software development growth, from early development through operations, testing, and deployment, to distribute high-quality software products reliably, quickly, and feasibly. In this manuscript, we derive the frank operational laws based on IL information for FTN and FTCN. Additionally, we analyze the ILFWA operator, ILFOWA operator, ILFWG operator, and ILFOWG operator. Some fundamental laws for the above operators are also derived. Moreover, using the techniques, we evaluate the TOPSIS technique, which will help in the investigation of the best optimal. Further, we illustrate some practical examples based on initiated techniques for showing the supremacy and validity of the derived theory with the help of MADM methods by evaluating the problem of DevOps. Finally, to show the supremacy and validity of the presented techniques, we aim to compare the proposed ranking values with some existing techniques.

The rise of smart health technologies has offered benefits such as remote patient monitoring, personalized therapy, and early disease detection. However this advancement has also raised concerns about security and privacy of patient data. Authentication is essential for protecting patient data and smart health devices. The existing scheme is identified with security vulnerabilities in terms of stolen verifier, impersonation, and man in middle attacks. Motivated by this, authors have presented a secure, lightweight authentication technique for smart health environments for a remote monitoring scenario employing a Multiple Factor approach and a lightweight algorithm. The scheme is verified through formal and informal analysis. The scheme demonstrated enhanced authentication performance and security with processing overhead of (0.04 milliseconds) and transmission overhead of (704 bits), in comparison to the baseline scheme. This system has the potential to improve data security and privacy in a variety of smart health scenarios.

Ever-increasing dynamic surges in renewable-based electric power systems, notably wind and photovoltaic farms bring adverse impacts and challenges in terms of reliability and stability. The intermittency of renewable sources imposes significant deviations in frequency due to variations in demand. Wind power induces instability in the grid due to its vulnerable nature, and reduction in system inertia. To mitigate these dynamics issues, an optimal control technique based on flatness-based Active disturbance rejection control (FADRC) and utilizing an enhanced Beluga Whale optimization algorithm (EBWO) for a multi-area interconnected power system with photovoltaic generation. The proposed LFC model addresses the load perturbation and the deviation of tie-line power, with system uncertainties considered as lumped disturbances that are approximated by extended state observers. To achieve optimal performance, the Enhanced Beluga Whale optimization algorithm is adopted and integrated with the suggested controller to fine-tune the controller. To validate the formidable performance of the suggested scheme, different cases have been studied with the existing approaches. The simulation results reveal the supremacy and robustness of the dynamic response of the Flatness-based active disturbance rejection control as compared to other approaches under load variations and parameter uncertainty.

Abstract Adventitious root (AR) formation is critical for cutting survival and nutrient absorption re-establishment. This complex genetic trait involves the interplay of nitrogen, endogenous hormones, and several key genes. In this study, we treated GL-3 apple (Malus domestica) in vitro shoots with nitrate and ammonium to determine their impact on AR formation, hormonal content, and gene expression patterns. Nitrate treatment significantly promotes adventitious rooting by increasing cell division, differentiation, and AR primordia formation compared to ammonium treatment. Elevated indole-3-acetic acid (IAA), reduced abscisic acid, and zeatin riboside concentrations were consistently observed with nitrate, likely crucial for promoting ARs over ammonium. Furthermore, Malus domestica auxin resistance1 (MdAUX1) expression was induced, increasing IAA levels. MdIAA23 was upregulated. Further results indicate that the higher expression levels of Malus domestica WUSCHEL-related Homeobox gene 11 (MdWOX11), Malus domestica lateral organ boundaries domain gene 16 (MdLBD16), and MdLBD29, and increased cell cycle-related gene expressions, contribute to auxin-stimulated adventitious rooting under nitrate conditions. In conclusion, this study establishes that auxin content and associated genes related to root development and cell cycle contribute to superior ARs in response to nitrate.

Background: Oral cancer is regarded as the sixth most common malignant tumor and is one of the leading cause of death. Lymph node metastasis is a major factor for the prognosis of oral squamous cell carcinoma (OSCC). The objective of study was to compare the lymph node metastases with different histopathological parameters to identify high risk patients with the need for extensive treatment plans. Methods: 140 histologically proven OSCC patients visiting Ziauddin College of Dentistry were included in this study. The clinico-pathological parameters were compared by using Chi-square, Kruskal-Willis and Mann-Whitney. Results: There were 90 male and 50 female patients with 29.8% in 49-58 years age group. Buccal mucosa was the most common site of tumor. The predominant morphology of OSCC was moderately differentiated OSCC with stage IV disease. Lymph node metastasis was present in 48.6% of patients. Perineural invasion (22.9%), lymphovascular invasion (8.6%) and distant metastasis (11.1%) were also recorded. The mean tumor size was 3.45mm ± 1.95mm and mean tumor thickness was 1.8 ± 1.6mm. Conclusion: A significant association between grade, stage, tumor size, tumor thickness and distant metastasis with lymph node involvement was found. However further studies with larger sample size are required to validate these results. Key Words: Oral squamous cell carcinoma; Lymph node metastases; Tumor size; tumor thickness.

The graphical user interface (GUI) in mobile applications plays a crucial role in connecting users with mobile applications. GUIs often receive many UI design smells, bugs, or feature enhancement requests. The design smells include text overlap, component occlusion, blur screens, null values, and missing images. It also provides for the behavior of mobile applications during their usage. Manual testing of mobile applications (app as short in the rest of the document) is essential to ensuring app quality, especially for identifying usability and accessibility that may be missed during automated testing. However, it is time-consuming and inefficient due to the need for testers to perform actions repeatedly and the possibility of missing some functionalities. Although several approaches have been proposed, they require significant performance improvement. In addition, the key challenges of these approaches are incorporating the design guidelines and rules necessary to follow during app development and combine the syntactical and semantic information available on the development forums. In this study, we proposed a UI bug identification and localization approach called Mobile-UI-Repair (M-UI-R). M-UI-R is capable of recognizing graphical user interfaces (GUIs) display issues and accurately identifying the specific location of the bug within the GUI. M-UI-R is trained and tested on the history data and also validated on real-time data. The evaluation shows that the average precision is 87.7% and the average recall is 86.5% achieved in the detection of UI display issues. M-UI-R also achieved an average precision of 71.5% and an average recall of 70.7% in the localization of UI design smell. Moreover, a survey involving eight developers demonstrates that the proposed approach provides valuable support for enhancing the user interface of mobile applications. This aids developers in their efforts to fix bugs.

In an islanded hybrid microgrid, frequency deviation is caused by the intermittent nature of renewable resources and load variations. Maintaining nominal frequency requires the secondary controller to send a signal to the controllable energy sources so that supply and demand are synchronized. Time delays in the system's communication channels, however, can reduce its efficiency. To compensate for the system uncertainties, modeling error and variant nature of renewable resources, an optimized flatness based active disturbance rejection controller have been proposed. Mitigating the effects of communication delay in the system and to synchronize the inputs to the extended state observer, a smith predictor based low pass filter is employed. Furthermore, an improved beluga whale optimization (IBWO) technique is employed to tune the proposed controller gains. The comparative analysis of the proposed IBWO is performed with existing algorithms. Moreover, the proposed controller superiority over the standard PID and LADRC approach has been investigated by perturbation in renewable resources, uncertainty in parameters, variation and mismatch in communication delay. Simulation results provide the testimony for the efficient performance of the designed controller. Lastly, the proposed controller is further evaluated in multi microgrid environment.

Emotion detection (ED) involves the identification and understanding of an individual’s emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person’s emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).

This paper presents a novel approach to studying divorce dynamics and elimination strategies using nonlinear differential equations. A mathematical model is formulated to capture the key factors influencing divorce rates. The model undergoes a rigorous theoretical analysis, including parameter estimation, solution existence/uniqueness, positivity, boundedness, and invariant regions. A qualitative analysis explores equilibria, stability conditions, and a sensitivity analysis. Numerical simulations and discussions are presented to validate the model and shed light on divorce dynamics. Finally, conclusions and future research directions are outlined. This work offers valuable insights for understanding and potentially mitigating divorce rates through targeted interventions.

Kerala's Idukki district, which is situated on the Western Ghats of India, is susceptible to flooding and landslides. As a result of the 2018 Kerala floods, this disaster-prone region experienced drought conditions. In order to lessen the effects of future disasters, it is also necessary to identify and evaluate the district's groundwater potential (GWP). This work used three machine-learning (ML) algorithms – Random Forest (RF), Adaptive Boosting (AdaBoost), and Gradient Boosting (GB) – to model and produce GWP zonation maps for the Idukki district. Fourteen conditioning factors including elevation, slope, curvature, Topographic Roughness Index, lineament density, soil, geology, geomorphology, Topographic Wetness Index, Sediment Transport Index, drainage density, rainfall, land-use/land-cover (LULC), and Normalised Difference Vegetation Index were adopted as input parameters in the modelling. All showed prominence when they were examined for feature importance using the recursive feature elimination (RFE) method. The RF model outperformed the other two ML models in terms of fit, with an area under curve (AUC) value of 0.92, while the GB and AdaBoost models displayed less fit, with AUC values of 0.90 and 0.88, respectively. GWP maps produced by each model were reclassified into five zones – very high to very low – it was discovered that the zones were evenly spread throughout the Idukki region. HIGHLIGHTS Groundwater potential zones were identified using different machine-learning models such as Random Forest (RF), Adaptive Boosting (AdaBoost), and Gradient Boosting.; Fourteen groundwater conditioning factors were utilised to evaluate the groundwater potential zones.; Findings revealed that among all the machine-learning models Random Forest yields the most accurate results.; Validation was performed using well locations.;

Abstract Background: Social determinants of health (SDoH), such as socioeconomics and neighborhoods, strongly influence health outcomes. However, the current state of standardized SDoH data in electronic health records (EHRs) is lacking, a significant barrier to research and care quality. Methods: We conducted a PubMed search using “SDOH” and “EHR” Medical Subject Headings terms, analyzing included articles across five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions. Results: Of 685 articles identified, 324 underwent full review. Key findings include implementation of tailored screening instruments, census and claims data linkage for contextual SDoH profiles, NLP systems extracting SDoH from notes, associations between SDoH and healthcare utilization and chronic disease control, and integrated care management programs. However, variability across data sources, tools, and outcomes underscores the need for standardization. Discussion: Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical for SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately, widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Type 2 diabetes mellitus (T2DM) is a rapidly escalating global health concern, with its prevalence projected to increase significantly in the near future. This review delves into the intricate role of epigenetic modifications - including DNA methylation, histone acetylation, and micro-ribonucleic acid (miRNA) expression - in the pathogenesis and progression of T2DM. We critically examine how these epigenetic changes contribute to the onset and exacerbation of T2DM by influencing key pathogenic processes such as obesity, insulin resistance, β-cell dysfunction, cellular senescence, and mitochondrial dysfunction. Furthermore, we explore the involvement of epigenetic dysregulation in T2DM-associated complications, including diabetic retinopathy, atherosclerosis, neuropathy, and cardiomyopathy. This review highlights recent studies that underscore the diagnostic and therapeutic potential of targeting epigenetic modifications in T2DM. We also provide an overview of the impact of lifestyle factors such as exercise and diet on the epigenetic landscape of T2DM, underscoring their relevance in disease management. Our synthesis of the current literature aims to illuminate the complex epigenetic underpinnings of T2DM, offering insights into novel preventative and therapeutic strategies that could revolutionize its management.

Objective: To compare derangement of cerebrospinal fluid markers in neonates with early and late-onset sepsis managed at the Neonatal Intensive Care Unit. Study Design: Comparative cross-sectional study. Setting and Duration of Study: Neonatology Department, Pak Emirates Military Hospital, Rawalpindi Pakistan, from Jul 2021 to Jun 2022. Methodology: This study was conducted on neonates managed at our Neonatal Intensive Care Unit for late or early-onset sepsis. A consultant neonatologist diagnosed neonatal sepsis based on clinical and laboratory findings. The cerebrospinal fluid routine examination was performed on all the study participants, and the derangement of various markers was compared in neonates with early and late-onset sepsis. Results: A total of 348 neonates admitted to the neonatal intensive care unit with sepsis were included in the final analysis. The mean age of the newborns included in the study was 5.38±4.53 days. Out of all the neonates in the study, 194(55.7%) had early-onset sepsis, while 154(44.3%) neonates had late-onset sepsis. All the cerebrospinal fluid markers were statistically significantly deranged in patients with late-onset sepsis (p-value

The discipline of Automatic Text Summarization (ATS), which is expanding quickly, intends to automatically create summaries of enormous amounts of text so that readers can save time and effort. ATS is a rapidly growing field that aims to save readers time and effort by automatically generating summaries of large volumes of text. In recent years, significant advancements have been witnessed in this area, accompanied by challenges that have spurred extensive research. The proliferation of textual data has sparked substantial interest in ATS, which is thoroughly examined in this survey study. Researchers have been refining ATS techniques since the 1950s, primarily categorized as extractive, abstractive, or hybrid approaches. In the extractive approach, key sentences are extracted from the source document(s) and combined to form the summary, while the abstractive approach employs an intermediary representation of the input document(s) to generate a summary that may differ from the original text. Hybrid approaches combine elements of both extractive and abstractive methods. Despite various recommended methodologies, the generated summaries still exhibit noticeable differences compared to those created by humans. This research survey offers an inclusive exploration of ATS, covering its challenges, types, classifications, approaches, applications, methods, implementations, processing and preprocessing techniques, linguistic analysis, datasets, and evaluation measures, catering to the needs of researchers in the field.

Hot topic trends have become increasingly important in the era of social media, as these trends can spread rapidly through online platforms and significantly impact public discourse and behavior. As a result, the scope of distributed representations has expanded in machine learning and natural language processing. As these approaches can be used to effectively identify and analyze hot topic trends in large datasets. However, previous research has shown that analyzing sequential periods in data streams to detect hot topic trends can be challenging, particularly when dealing with large datasets. Moreover, existing methods often fail to accurately capture the semantic relationships between words over different time periods, limiting their effectiveness in trend prediction and relationship analysis. This paper aims to utilize a distributed representations approach to detect hot topic trends in streaming text data. For this purpose, we build a sequential evolution model for a streaming news website to identify hot topic trends in streaming text data. Additionally, we create a visual display model and knowledge graph to further enhance our proposed approach. To achieve this, we begin by collecting streaming news data from the web and dividing it chronologically into several datasets. In addition, word2vec models are built in different periods for each dataset. Finally, we compare the relationship of any target word in sequential word2vec models and analyze its evolutionary process. Experimental results show that the proposed method can detect hot topic trends and provide a graphical representation of any raw data that cannot be easily designed using traditional methods.

Deceptive jamming is one of the indispensable part of electronic countermeasure technology, which has a significant role in modern warfare to secure the aircrafts from imminent threats. This research work proposes a novel Frequency Diverse Array-Multiple Input Multiple Output (FDA-MIMO) deceptive jamming method for neutralizing the opponent Phased Array Radar (PAR). The proposed deceptive jamming model works in passive mode. It interrupts the opponent radar signals and after addition of the deceptive signals, the processed signals are propagated back to neutralize the surveillance operation of the intended radar. When the opponent PAR receives these signals, it perceives multiple fake aircrafts flying at different ranges along the actual aircraft with modified and up-shifted radial velocity. Further, the proposed model uses diversification in frequencies, waveforms, and time modulation to protect the flight of the preferred aircraft. The effectiveness of the proposed model is verified through several simulations in Matlab using different number of arrays, directions and ranges.

The echo state network (ESN) is a cutting-edge reservoir computing technique designed to handle time-dependent data, making it highly effective for addressing time series prediction tasks. ESN inherits the more precise design of standard neural networks and the relatively simple learning process and has a strong computing capacity for solving nonlinear problems. It can disseminate low-dimensional information cues to high-dimensional areas enabling extracting data. However, this study has proven that not all reservoir output dimensions directly impact model generalization. This study desires to enhance the ESN model’s generalization abilities by decreasing the redundant reservoir output feature. A remarkable hybrid model is proposed that optimizes the ESN output association through feature selection. This model is called the binary improved gravitational search algorithm (BIGSA) echo state network (BIGSA-ESN). BIGSA’s feature selection approach complements the ESN output connection architecture. In this study, evaluation was performed using root mean square error (RMSE). The experimental findings on the Lorenz and Mackey-Glass benchmark time-series datasets demonstrate that the proposed technique outperforms conventional evolutionary methods. Moreover, empirical findings on predicting a significant water quality parameter from the wastewater treatment process (WWTP) dataset demonstrate that the proposed ensemble of BIGSA models performs very well in real-world scenarios.

Enterprise Architecture (EA) is crucial in any organisation as it defines the basic building blocks of a business. It is typically presented as a set of documents that help all departments understand the business model. In EA, safety documents are used to manage and understand safety risks. A novel similarity system for railway safety document processing is presented in this work. It measures the feasibility of automated updating of EA models with the Rule Book by verifying whether Rail Safety and Standards Board (RSSB’s) Rule Book clauses are present and complete in existing EA models. Additionally, a Natural Language Processing (NLP) based search feature was developed to drill through the database to find similar existing rules, principles, and clauses based on semantic similarity. The result will display the most similar clauses and rules with similarity scores and document names. In this study, different pre-trained Electra Small, DistilBERT (Distillation Bidirectional Encoder Representations from Transformers) Base and BERT (Bidirectional Encoder Representations from Transformers) Base were used to embed text. Additionally, the similarity between document rules was measured by cosine similarity metrics. With conclusive evidence, our findings show that BERT Base exceeds the other embedding methods in the semantic comparison of documents.

Aims: Chronic osteomyelitis (COM) of the lower limb in adults can be surgically managed by either limb reconstruction or amputation. This scoping review aims to map the outcomes used in studies surgically managing COM in order to aid future development of a core outcome set. Methods: A total of 11 databases were searched. A subset of studies published between 1 October 2020 and 1 January 2011 from a larger review mapping research on limb reconstruction and limb amputation for the management of lower limb COM were eligible. All outcomes were extracted and recorded verbatim. Outcomes were grouped and categorized as per the revised Williamson and Clarke taxonomy. Results: A total of 3,303 records were screened, of which 99 studies were included. Most studies were case series (77/99; 78%) and assessed one method of reconstruction (68/99; 69%). A total of 511 outcomes were reported, which were grouped into 58 distinct outcomes. Overall, 143/511 of all outcomes (28%) were provided with a clear, in-text definition, and 231 outcomes (45%) had details reported of how and when they were measured. The most commonly reported outcome was ‘recurrence of osteomyelitis’ (62; 12%). The single-most patient-reported outcome measure was ‘pain’. Conclusion: This study has highlighted significant inconsistencies in the defining, reporting, and measuring of outcomes across studies investigating surgical management for chronic osteomyelitis of the lower limb in adults. Future studies should clearly report complete details of how outcomes are defined and measured, including timing. The development of a standardized core outcome set would be of significant benefit in order to allow evidence synthesis and comparison across studies. Cite this article: Bone Jt Open 2023;4(3):146–157.

The diesel generators sets (DGs) and battery storage systems (BSS) are the essential energy sources in a modern high-rise buildings. In this paper DG, BSS and Photovoltaic system (PV) has been considered to minimize the grid power injection using a centralized Energy Management System (EMS). Machine Learning (ML) techniques are used to predict the performance of various regression models by comparing grid power and load curves. It includes Artificial Neural Network (ANN), Wide Neural Network (WNN), Linear Regression (LR), Linear Regression Interaction (LR-I), Linear Regression Stepwise (LR-S), Regression Fine Tree (RF-T), Regression Coarse Tree (RC-T) and Gaussian Process Regression (GPR) based techniques. The Demand Side Management (DSM) techniques such as peak shaving and valley filling is integrated with ML technique in a Hybrid energy source (HS) system.The comparative analysis of results depicts the effective reshaping of the grid profile without scheduling or disconnecting the loads. Matlab simulation software is used to validate the results.

Computational fluid dynamics is a compelling apparatus for getting stream flow and anticipating how this flow will react to various limiting boundary conditions. With this learning, the focal point of this research is applying computational fluid elements such as CFD to issues dealing with stream flow measurement/estimation in closed conduits such as pipes utilizing differential stream meters like the Venturi meter. After thorough research from the existing literature, it was determined that the convergent angle (CA) of a standard Venturi meter has not been optimized yet. The range given for a standard ASME Venturi CA is 20–22°. More than 50 models were created and run in ANSYS FLUENT, which was used as a CFD tool. Three β-ratios are taken into consideration here, which are 0.4, 0.5 and 0.6. An optimum value of CA, corresponding to each β, is obtained by finding out the best coefficient of discharge (closest to 0.99) for each test value of CA. Another aspect explored in this research is the relationship between Reynolds number and coefficient of discharge. This is done with the integration of ANSYS FLUENT and laboratory results. The results of this study yield a definite value of CA for each β. HIGHLIGHTS The focal point of this research is the optimization of the convergent angle of the Venturi meter.; More than 50 models were created and run in ANSYS FLUENT, and three β-ratios are taken into consideration here, which are 0.4, 0.5 and 0.6.; An optimum value of convergent angle, corresponding to each β, is obtained by finding out the best coefficient of discharge (closest to 0.99) for each test value of convergent angle.;

Women in the pre-revolution Russia lived in a miserable predicament. Relegated to subservient positions, they were denied volition, were often treated like slaves, and were left at the mercy of fate. This social crisis finds voice in Russian fiction which is on most occasions reflective of women’s plight: Maxim Gorky’s Mother is one such instance. It examines the tragic plight of the Russian women in the pre-revolution milieu. This paper reads the female protagonist of Mother, Nilovna in light of the existentialist notions, mainly those espoused by Jean-Paul Sartre, exploring how Nilovna finds her way out from sheer passivity and deterministic domesticity to the world of possibility and volition by marching forth with the revolutionaries, playing her significant role in the revolutionary movement. Using the broad theorization of existentialism, focusing precisely on Sartre’s notions of bad faith and choice of free will, the study explores how a miserable woman in her existentialist journey traverses from the mundane domesticity to the purposeful revolution as an active political worker committed to the cause of revolution. The study foregrounds how Nilovna finds a purpose in her meaningless and passive life by helping her son who becomes a socialist after his father's death.

Heavy metals are one of the factors that pollute the environment and significantly affect soil fertility, plant physiology, development, and productivity. The tolerance of plants to toxicity depends on the species and tissue, element type, and duration of exposure to stress. Some special signal molecules such as nitric oxide (NO), hydrogen peroxide (H2O2), beneficial ions, hyperaccumulating plants, stress hormones, nanoparticles, organic compounds, and microbial applications can be recommended to alleviate the stress effects caused by toxic heavy metals in plants. Induction of other promising techniques like seed priming, active involvement of plant growth regulator, use of osmoprotectants, successful plant microbes’ crosstalk and recent utilization of nanoparticles are worth using strategies in mitigation of heavy metal stress in plants. These practices effectively regulate the activities of antioxidant enzymes for the alleviation of stress in plants, creditably improving the plant tolerance via preserving cell homeostasis and amending the adversative effects of heavy metal stress in plants. These inventive strategies offer an enriched understanding of how to boost crop productivity under heavy metal stress in order to decrease the risk to global food security.

Water is the most wonderful substance. Almost 70% of our planet consists of water. The human body comprises almost 60% of water. Safe and clean water is necessary for all purposes in daily life which include different practices like washing, drinking, cooking, and personal cleanliness(Frances and Brack, 2018). Water is known asa universal solvent because it can dissolve a large proportion of natural or man-made substances(Balasubramanian, 2015). Fecal contaminated water is the main reason for waterborne disease. With quick urbanization, the chemical feature of water quality has contributed to increasing concerns as toxic chemicals industrial effluents pose a high hazard to life(Alurralde et al., 1998). Pakistan has been blessed with water resources, but unfortunately over the years, industrial development, overpopulation, and rapid growth have decelerated the water resources (Daud et al., 2017). Different studies show that the majority of the supplies of potable water are polluted (Aziz, 2005). Pakistan ranks 80 out of 122 nations, and both ground and surface drinking water sources are polluted throughout the country with toxic metals, microbes, and pesticides(Nabeela et al., 2014). Even currently some rural areas in Pakistan have no availability of fresh and clean water for regular use (Shahid et al., 2014). The quality of drinking water in Pakistan is reducing day by day due to the waste and pollutants released by the industries. Channeled water also gets contaminated because the network of the pipeline is not planned and laid poorly (Imran et al., 2018).

Assessment of gait deficits relies on accurate gait segmentation based on the key gait events of heel strike (HS) and toe-off (TO). Kinematics-based estimation of gait events has shown promise in this regard, especially using the leg velocity signal and gyroscopic sensors. However, its validation for the amputee population is not established in the literature. The goal of this study is to assess the accuracy of lower-leg angular velocity signal in determining the TO and HS instants for the amputee population. An open data set containing marker data of 10 subjects with unilateral transfemoral amputation during treadmill walking was used. A rule-based dual-minima algorithm was developed to detect the landmarks in the shank velocity signal indicating TO and HS events. The predictions were compared against the force platform data for 2595 walking cycles from 239 walking trials. The results showed considerable accuracy for the HS with a median error of −1 ms. The TO prediction error was larger with the median ranging from 35–84 ms. The algorithm consistently predicted the TO earlier than the actual event. Significant differences were found between the prediction accuracy for the sound and prosthetic legs. The prediction accuracy was also affected by the subjects’ mobility level (K-level) but was largely unaffected by gait speed. In conclusion, the leg velocity profile during walking can predict the heel-strike and toe-off events for the transfemoral amputee population with varying degrees of accuracy depending upon the leg side and the amputee’s functional ability level.

In this paper, a D-type photonic crystal fiber (PCF) with Zeonex material as the substrate and polyvinylidene fluoride (PVDF) material as the surface plasmon resonance (SPR) excitation layer is proposed for biosensing in the terahertz (THz) band. Analyzed with a finite element method, the proposed biosensor has shown excellent sensing properties for analyte refractive indices ranging from 1.32 to 1.45. With a maximum sensor resolution of 8.40 × 10−7 refractive index unit (RIU) and a figure of merit of 39.42 RIU−1, the maximum wavelength sensitivity and amplitude sensitivity can reach 335.00 μm/RIU and −66.01 RIU−1, respectively. A ±2% fabrication tolerance analysis is also performed on the biosensor to prove its practical feasibility. We conclude that our proposed PCF biosensor utilizing PVDF-excited SPR can provide high sensitivity, and thus a compact, label-free, and convenient solution for biomedical liquid sensing in the THz band.

Accurate estimation of gait parameters from the limb kinematics data remains a challenge for researchers. Several deterministic algorithms have been proposed in the literature for this purpose but their applicability is limited by their accuracy, especially for gait-impaired subjects. Gait parameter estimation relies on the accurate detection of key gait events of initial contact (IC) and terminal contact (TC) for gait segmentation. This study aimed to evaluate literature algorithms for IC and TC prediction for the amputee population and to introduce a novel algorithm to improve gait parameter estimation. A marker data set of 12 amputee subjects during treadmill walking was used. Seven algorithms, including one novel algorithm, detecting the landmarks (maxima, minima, and zero-crossings) in the foot, shank, and thigh velocity data were implemented and their IC and TC estimations were compared against the force platform data. IC was best detected by foot and shank velocity methods with median bias in the range of −3 to −27ms. However, the literature algorithms produced inferior accuracy for the TC event with consistent early prediction. The newly-proposed thigh velocity-based algorithm produced the best result for the TC prediction −14 to 19ms median bias. By combining the IC prediction from foot velocity data and the TC prediction from the thigh velocity, the algorithm produced the best results for temporal gait parameters with a median bias of within 5% of gait cycle time. Statistical analysis revealed the robustness of the algorithm across most walking speeds.

China's Belt and Road Initiative (BRI) is the foreign policy strategy that has changed world politics. It has compelled states to revisit their foreign policies either in favour of BRI or countering it. BRI has created a difficult situation for international power structure and status quo states due to transitioning power from west to east and emerging geopolitical reconfiguration. However, in September 2021, President Xi Jinping announced China's Global Development Initiative (GDI) at the opening session of the UN General Assembly, which renewed debate about the impacts of China's strategy. As China has a strong foothold in the Indian Ocean region through BRI, the undertaken research is an endeavour to study the effect of shifting China's foreign policy from BRI to GDI over the maritime geopolitics of the region. This research aims at determining GDI's role in supporting UN Agenda 2030 through focused efforts on sustainable development goals in the maritime domain. It is an exploratory and qualitative research conducted through deductive reasoning to seek an explanation of the core proposition. This research has used secondary sources for determining these upshots with potential changes in the maritime geopolitics of the Indian Ocean region. Bibliography Entry Khan, Maliha Zeba, and Zohaib Altaf. 2022. "China's Foreign Policy Shift from Belt and Road Initiative to Global Development Initiative: Impacts on Maritime Geopolitics of the Indian Ocean Region." Margalla Papers 26 (2): 14-27.

Abstract The authors present the parallel‐hybrid recursive least square (PH‐RLS) algorithm for an accurate self‐mixing interferometric laser vibration sensor coupled with an accelerometer under industrial conditions. Previously, this was achieved by using a conventional RLS algorithm to cancel the parasitic vibrations where the sensor itself is not in the stationary environment. This algorithm operates in sequential mode and due to its compute and data‐intensive nature, the algorithm does not work for real‐time applications, hence requires parallel computing. Therefore, the existing conventional RLS C program is parallelized by using hybrid OpenACe C/MPI (Open Accelerators/Message Passing Interface) parallel programming models and tested on Barcelona Supercomputing Center CTE‐Power9 Supercomputer. The computational performance of the proposed PH‐RLS algorithm is compared with the existing conventional RLS code by executing on multi distributed processors and uni‐core processor architecture, respectively. While comparing the performance of conventional RLS with a PH‐RLS algorithm on eight nodes of CTE‐Power9 supercomputer, the results show that the PH‐RLS algorithm gets 5857 times of performance improvement as compared to the conventional RLS implementation on a single node system. The results show that the proposed PH‐RLS also gives a scalable performance for a different range of vibration signals, making it a suitable choice for real‐time self‐mixing interferometer sensing systems working under industrial conditions.

Cucurbits are important vegetable crops of Pakistan which are severely affected by various diseases. Keeping in view the importance of viral diseases, the present study was aimed to conduct systematic surveys of the tunnel and open field cucurbit crops in Punjab, Pakistan during 2019 and 2020. The cucurbits leaf samples with virus and virus-like symptoms were collected from the farmers fields and tunnels with no prior knowledge about sanitation and subjected to reverse transcription-polymerase chain reaction (RT-PCR) for poleroviruses incidence. The mean highest disease incidence was recorded in samples collected from district Rawalpindi (47.42%) and the mean lowest disease incidence was observed in samples of district Faisalabad (30.95%) with an overall disease incidence of 40.48% observed during 2019 and 2020. Newly detected Pakistani CABYV isolates shared 99.6 % and 94.7-96.2% nucleotide identities among themselves and with isolates retrieved from NCBI GenBank. Phylogenetic analysis showed that current study isolates i.e., OL828566 and OL828567 clustered in separate clades with isolates JQ700305, JF939814 and X7693 from Taiwan, Spain and France, respectively while isolates from Thailand, China, and Korea were grouped separately. It was evident with restriction fragment length polymorphism (RFLP) analysis that the present study CABYV isolates were grouped into two types, which seemed to be genetically similar to those identified during 2011–2015 and 2015–2021. Recombination detection analysis showed that newly detected CABYV isolates are likely to be recombinant of Spanish (JF939814) and South Korean (KR231950) isolates having the recombination breakpoint between nucleotide position number 272 and 830. Comparison and recombination detection analysis of the local isolates might help in devising a breeding program to identify resistant sources against recombinant isolates.

Neonatal diabetes is estimated to affect 1 in every 95,000 to 1 in every 400,000 live births. Male and female infants are equally affected by the disease, which has been recorded in all ethnic groups. Neonatal diabetes mellitus is a type of diabetes that develops within the first six months of life. Insulin is a hormone that aids in the production of energy in our cells. This condition causes infants to produce insufficient insulin, resulting in elevated blood glucose levels. Therefore, it is very important to design a testing device that can monitor premature babies indicating symptoms of diabetes. Recognizing the signs of diabetes in an infant might be difficult because diabetes in babies is not common. Frequent wet diapers, a high appetite, dehydration, and weight loss are all signs of neonatal diabetes. Once you have diabetes, you're used to having to take tests to monitor your condition. Conventionally invasive methods are used to check neonatal diabetes which is a painful procedure for infants as it requires prinking to draw blood several times per day. Therefore, this paper aims to design a non-invasive testing design for neonatal diabetes that is not painful to babies. There are several non-invasive ways to test blood glucose levels; urine, sweat, and saliva. Urine tests can be used to detect glucose levels in urine and check for the presence of ketones. The presence of ketone above the threshold indicates a high blood glucose level of over 300ml/dL. Sweat is also used to test diabetes. Hyperhidrosis (excessive sweating) is caused by high blood sugar levels, and it indicates that blood sugar control has to be tightened. Saliva is a new medium to measure blood glucose levels and research is going on its use to measure diabetes. Previous research has found a strong relationship between blood glucose levels and saliva glucose levels. In this paper, saliva is used as a non-invasive method to measure neonatal diabetes. Saliva containing glucose is collected and treated with glucose oxidase. H2O2 produced during this reaction is further treated and an optical sensor along with a microcontroller is used to measure the concentration of blood glucose. The expected results based on the study indicate the amount of glucose in the saliva increases in proportion to the amount of glucose in the blood and confirms the relationship between glucose concentrations in the blood and saliva, implying that the proposed design can take the place of the existing devices. However, more tests should be carried out to improve the efficacy of the proposed design for detecting glucose in saliva.

In hospitals the monitoring of patient is usually done manually with the help of nursing staff for 24 x 7. The availability of nursing staff to monitor the health of critically ill patients or new born infant is very difficult task and requires a lot of man power. In case, of absence or delay the health of patient can become critical and can be fatal. In order to solve this many wireless technologies have been proposed to monitor the patient’s condition using different sensors but these wireless schemes are harmful for patients/infants and can even interface with medical devices. In order to develop hospital friendly monitoring system, Li-Fi based health monitoring-based system has been proposed which measure the heart rate, temperature and motion in case of infants and the data is continuously displayed on LCD. In case of any abnormalities, the relevant staff will be notified.

Background: Smokeless tobacco (ST) use is common among youth in South Asia where 85% of the world’s 300 million ST users live and use the most lethal ST forms. Little is known about the impact of tobacco control policies on the youth ST uptake in those countries. We planned to conduct longitudinal surveys among school going adolescents to evaluate existing tobacco control policies on tobacco uptake and use, and a feasibility study for that prospective, observational cohort study. Study objectives: (1) To demonstrate the feasibility of selection, recruitment and retention of schools and of study participants; (2) To assess the feasibility and acceptability of the study procedure and study tool (questionnaire); (3) To evaluate if the questionnaire can assess tobacco uptake and use, and their potential predictors. Methods and analysis: The feasibility study will be conducted in two administrative areas within each of three South Asian countries: Bangladesh, India and Pakistan. We will use both quantitative and qualitative data collection methods. Eight eligible schools will be randomly selected within purposively selected sub-districts from each country. We plan to conduct one baseline and one follow up survey among students of grade 6-8, one year apart. At each time point, data on tobacco uptake and potential predictors will be collected from students via self-administered questionnaires that were designed for the longitudinal study. The qualitative component will be embedded into the study with each round of data collection to assess the acceptability of the study instrument (questionnaire) and data collection methods, via focus group discussions with students and semi-structured interviews with schoolteachers. Recruitment and retention rates, completeness of the questionnaires, frequencies and associations of tobacco use and explanatory variables will be reported. Data gathered from the focus group and interviews will be analysed using the framework approach.

Background and study aims The impact of guidewire caliber on endoscopic retrograde pancreatography (ERCP) outcomes are not clear. Recent studies have compared two guidewires, 0.035- and 0.025-inch, in randomized controlled trials (RCTs). We performed a systematic review and meta-analysis of available RCTs to assess if different caliber would change the outcomes in ERCP. Patients and methods A systematic search of PubMed/Medline, Embase, Cochrane, SciELO, Global Index Medicus and Web of Science was undertaken through November 23, 2021 to identify relevant RCTs comparing the two guidewires. Binary variables were compared using random effects model and DerSimonian-Laird approach. For each outcome, risk-ratio (RR), 95 % confidence interval (CI), and P values were generated. P

Background and study aims Recent studies evaluated the impact of i-scan in improving the adenoma detection rate (ADR) compared to high-definition (HD) colonoscopy. We aimed to systematically review and analyze the impact of this technique. Methods A thorough search of the following databases was undertaken: PubMed/Medline, EMBASE, Cochrane and Web of Science. Full-text RCTs and cohort studies directly comparing i-scan and HD colonoscopy were deemed eligible for inclusion. Dichotomous outcomes were pooled and compared using random effects model and DerSimonian-Laird approach. For each outcome, relative risk (RR), 95 % confidence interval (CI), and P value was generated. P

Abstract Background A low pH environment is created due to the production of acids by oral biofilms that further leads to the dissolution of hydroxyapatite crystal in the tooth structure significantly altering the equilibrium. Although the overall bacterial counts may not be eradicated from the oral cavity, however, synthesis of engineered anti-bacterial materials are warranted to reduce the pathogenic impact of the oral biofilms. The purpose of this study was to synthesize and characterize chlorhexidine (CHX)-loaded mesoporous silica nanoparticles (MSN) grafted with poly-L-glycolic acid (PGA) and to test the in vitro drug release in various pH environments, cytotoxicity, and antimicrobial capacity. In addition, this study aimed to investigate the delivery of CHX-loaded/MSN-PGA nanoparticles through demineralized dentin tubules and how these nanoparticles interact with tooth dentin after mixing with commercial dentin adhesive for potential clinical application. Results Characterization using SEM/TEM and EDX confirmed the synthesis of CHX-loaded/MSN-PGA. An increase in the percentage of drug encapsulation efficiency from 81 to 85% in CHX loaded/MSN and 92–95% in CHX loaded/MSN-PGA proportionately increased with increasing the amount of CHX during the fabrication of nanoparticles. For both time-periods (24 h or 30 days), the relative microbial viability significantly decreased by increasing the CHX content (P 80% indicating low cytotoxicity profiles of experimental nanoparticles. After 9 months in artificial saliva (pH 7.4), the significantly highest micro-tensile bond strength value was recorded for 25:50 CHX/MSN and 25:50:50 CHX/MSN-PGA. A homogenous and widely distributed 50:50:50 CHX-loaded/MSN-PGA nanoparticles exhibited excellent bonding with the application of commercially available dentin adhesive. Conclusions A pH-sensitive CHX release response was noted when loaded in MSN grafted PGA nanoparticles. The formulated drug-loaded nanocarrier demonstrated excellent physicochemical, spectral, and biological characteristics. Showing considerable capacity to penetrate effectively inside dentinal tubules and having high antibacterial efficacy, this system could be potentially used in adhesive and restorative dentistry.

Tumor segmentation in Computed Tomography (CT) images is a crucial step in image-guided surgery. However, low-contrast CT images impede the performance of subsequent segmentation tasks. Contrast enhancement is then used as a preprocessing step to highlight the relevant structures, thus facilitating not only medical diagnosis but also image segmentation with higher accuracy. In this paper, we propose a goal-oriented contrast enhancement method to improve tumor segmentation performance. The proposed method is based on two concepts, namely guided image enhancement and image quality control through an optimization scheme. The proposed OPTimized Guided Contrast Enhancement (OPTGCE) scheme exploits both contextual information from the guidance image and structural information from the input image in a two-step process. The first step consists of applying a two-dimensional histogram specification exploiting contextual information in the corresponding guidance image, i.e. Magnetic Resonance Image (MRI). In the second step, an optimization scheme using a structural similarity measure to preserve the structural information of the original image is performed. To the best of our knowledge, this kind of contrast enhancement optimization scheme using cross-modal guidance is proposed for the first time in the medical imaging context. The experimental results obtained on real data demonstrate the effectiveness of the method in terms of enhancement and segmentation quality in comparison to some state-of-the-art methods based on the histogram.

Solar-Powered Smart Irrigation System (SPSIS) is a solution to many problems of the agricultural system. In this work, a solar-powered smart irrigation solution is proposed for the farmers of Pakistan facing energy crises. The proposed smart irrigation system is powered by renewable solar energy, which is equipped with solar panel modules to convert sunlight into electrical energy. Arduino microcontroller-based system is designed for automatic operation of the complete system. Due to the automatic working of the proposed system, it will be better than a conventional irrigation system in terms of human effort, time, water and energy consumption to operate the irrigation system. The proposed Solar-Powered Smart Irrigation System (SPSIS) does not rely on grid power due to its self-energy production using solar power, resulting in a significant reduction of power usage from grid power. The proposed SPSIS is equipped with multiple input soil moisture sensors, which measure the humidity of the soil. SPSIS is also equipped with a monitoring setup to intimate the farmers about the water level in the field and the irrigation status of the field. Furthermore, SPSIS is also equipped with an automatic spray system to prevent weeds, insects and pests in the crops. Additionally, a GSM Module has also been added to the overall system for the intimation/controlling of the irrigation process through text messages. Such an implementation of a solar-powered smart irrigation system with additional features of automatic spray and monitoring and control using GSM will be highly beneficial for the formers in terms of power, efforts and resources required for irrigation, spray and monitoring of the crop.